The computer model shows the destructive potential of social networks


An interdisciplinary team of sociologists and physicists developed a computer model to analyze the ever-increasing political polarization. The multi-agent model aims in particular to clarify the role of social media in this regard. The study, that the researchers have now published in the journal PLOS ONE paints a pessimistic picture: depending on the model, there are tipping points at which social polarization becomes irreversible.

So far this is the case echo chamber effect As a common explanation for political polarization in social media: As social media users prefer to consume content that matches their worldview, their political opinion would solidify further – according to the theory. This approach is controversial, however, as social networks are usually not the primary sources of information for many users. So your effect is much smaller than initially expected. In addition, there are studies that show a paradoxical finding: if users are deliberately exposed to foreign and contrary views as much as possible, their political positions will not become more open and flexible, but they will harden again.

The sociologist Petter Törnberg from the University of Amsterdam and his colleagues from Germany, Italy and Sweden assume that political polarization is an effect that can be explained by the growing influence of political identities. According to this approach, political debates are no longer rational discussions of political disagreements. Rather, they resemble a struggle between warring tribes, in which it is more important to know who belongs in the group and who belongs to the “others” than who makes what arguments.

More from MIT Technology Review

More from MIT Technology Review

More from MIT Technology Review

The strength of the respective political identity therefore depends on interactions with like-minded people. If, on the other hand, you have a lot to do with people from different groups, the connection to the identity of the group weakens. To test the theory, the researchers developed a computer model to study the social dynamics of identity and political polarization. In it, the software agents interact with other agents selected at random in their immediate vicinity and with other agents whose political identity was similar to theirs – whom they had contacted according to their preference among the total pool of all agents. From the sum of all the interactions, it is then calculated in each unit of time of the model whether the link with political identity strengthens, weakens, or even if the identity changes completely.

While investigating the dynamics of the model, the team stumbled upon tipping points – degrees of polarization that trigger feedback loops that lead to an ongoing escalation of political polarization. In addition, the model shows so-called “hysteresis” effects, i.e. even if conditions were to change such that it would be much more difficult to find radical groups on social networks, the polarization would not fall below a threshold. some value again. “Just like climate change, political polarization can also react in unpredictable and dangerous ways,” says Törnberg, the study’s lead author.

In order to reduce polarization again, the separate groups should come together to work towards a common goal. “In the past, this task was fulfilled by the mass mobilization that characterized modern Fordist society, for example through large-scale wars,” writes Törnberg. “Today, in a postmodern and fragmented society, it is less clear how such a gathering could be implemented. About two years ago, a common response from researchers was that a large and shared problem – like a global pandemic – might offer a solution. Unfortunately, that doesn’t seem to have helped. “Even if he did not rely on a technical solution to the problem in principle, he was certain” that it would be entirely possible to introduce a depolarizing form of social media. “


Disclaimer: This article is generated from the feed and is not edited by our team.

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